{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2007-01-01'\n",
        "end = '2018-12-31'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-03</th>\n",
              "      <td>20.080000</td>\n",
              "      <td>20.400000</td>\n",
              "      <td>19.350000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>28350300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-04</th>\n",
              "      <td>19.660000</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.320000</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>23652500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-05</th>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.910000</td>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>15902400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-08</th>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>15814800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-09</th>\n",
              "      <td>19.450001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>14494200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume\n",
              "Date                                                                       \n",
              "2007-01-03  20.080000  20.400000  19.350000  19.520000  19.520000  28350300\n",
              "2007-01-04  19.660000  19.860001  19.320000  19.790001  19.790001  23652500\n",
              "2007-01-05  19.540001  19.910000  19.540001  19.709999  19.709999  15902400\n",
              "2007-01-08  19.709999  19.860001  19.370001  19.469999  19.469999  15814800\n",
              "2007-01-09  19.450001  19.709999  19.370001  19.650000  19.650000  14494200"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Open_Close'] = (dataset['Open'] - dataset['Adj Close'])/dataset['Open']\n",
        "dataset['High_Low'] = (dataset['High'] - dataset['Low'])/dataset['Low']\n",
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Open_Close</th>\n",
              "      <th>High_Low</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-04</th>\n",
              "      <td>19.660000</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.320000</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>19.790001</td>\n",
              "      <td>23652500</td>\n",
              "      <td>-0.006612</td>\n",
              "      <td>0.027950</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.013832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-05</th>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.910000</td>\n",
              "      <td>19.540001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>15902400</td>\n",
              "      <td>-0.008700</td>\n",
              "      <td>0.018935</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.004043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-08</th>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.860001</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>19.469999</td>\n",
              "      <td>15814800</td>\n",
              "      <td>0.012177</td>\n",
              "      <td>0.025297</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.012177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-09</th>\n",
              "      <td>19.450001</td>\n",
              "      <td>19.709999</td>\n",
              "      <td>19.370001</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>19.650000</td>\n",
              "      <td>14494200</td>\n",
              "      <td>-0.010283</td>\n",
              "      <td>0.017553</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.009245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-10</th>\n",
              "      <td>19.639999</td>\n",
              "      <td>20.020000</td>\n",
              "      <td>19.500000</td>\n",
              "      <td>20.010000</td>\n",
              "      <td>20.010000</td>\n",
              "      <td>19783200</td>\n",
              "      <td>-0.018839</td>\n",
              "      <td>0.026667</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.018321</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
              "Date                                                                          \n",
              "2007-01-04  19.660000  19.860001  19.320000  19.790001  19.790001  23652500   \n",
              "2007-01-05  19.540001  19.910000  19.540001  19.709999  19.709999  15902400   \n",
              "2007-01-08  19.709999  19.860001  19.370001  19.469999  19.469999  15814800   \n",
              "2007-01-09  19.450001  19.709999  19.370001  19.650000  19.650000  14494200   \n",
              "2007-01-10  19.639999  20.020000  19.500000  20.010000  20.010000  19783200   \n",
              "\n",
              "            Open_Close  High_Low  Increase_Decrease  Buy_Sell_on_Open  \\\n",
              "Date                                                                    \n",
              "2007-01-04   -0.006612  0.027950                  0                 0   \n",
              "2007-01-05   -0.008700  0.018935                  0                 1   \n",
              "2007-01-08    0.012177  0.025297                  0                 0   \n",
              "2007-01-09   -0.010283  0.017553                  1                 1   \n",
              "2007-01-10   -0.018839  0.026667                  1                 1   \n",
              "\n",
              "            Buy_Sell   Returns  \n",
              "Date                            \n",
              "2007-01-04         0  0.013832  \n",
              "2007-01-05         0 -0.004043  \n",
              "2007-01-08         1 -0.012177  \n",
              "2007-01-09         1  0.009245  \n",
              "2007-01-10         1  0.018321  "
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "(3019, 12)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 3019 entries, 2007-01-04 to 2018-12-31\n",
            "Data columns (total 12 columns):\n",
            "Open                 3019 non-null float64\n",
            "High                 3019 non-null float64\n",
            "Low                  3019 non-null float64\n",
            "Close                3019 non-null float64\n",
            "Adj Close            3019 non-null float64\n",
            "Volume               3019 non-null int32\n",
            "Open_Close           3019 non-null float64\n",
            "High_Low             3019 non-null float64\n",
            "Increase_Decrease    3019 non-null int32\n",
            "Buy_Sell_on_Open     3019 non-null int32\n",
            "Buy_Sell             3019 non-null int32\n",
            "Returns              3019 non-null float64\n",
            "dtypes: float64(8), int32(4)\n",
            "memory usage: 259.4 KB\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(dataset['Adj Close'])"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x24827291828>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(dataset.corr())"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x248272c9f60>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low','Volume', 'Open_Close', 'High_Low', 'Returns']]\n",
        "y = dataset['Adj Close']"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                   test_size=0.4, random_state=101)"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lm = LinearRegression()\n",
        "lm.fit(X_train,y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(lm.intercept_)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.0132862970476\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coeff_df = pd.DataFrame(lm.coef_,X.columns,columns={'Coefficient'})\n",
        "coeff_df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Open</th>\n",
              "      <td>-3.408460e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High</th>\n",
              "      <td>6.182590e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low</th>\n",
              "      <td>7.261640e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Volume</th>\n",
              "      <td>-2.454535e-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Open_Close</th>\n",
              "      <td>-1.527982e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High_Low</th>\n",
              "      <td>2.646308e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Returns</th>\n",
              "      <td>4.112396e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Coefficient\n",
              "Open       -3.408460e-01\n",
              "High        6.182590e-01\n",
              "Low         7.261640e-01\n",
              "Volume     -2.454535e-10\n",
              "Open_Close -1.527982e+00\n",
              "High_Low    2.646308e-01\n",
              "Returns     4.112396e-01"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = lm.predict(X_test)\n",
        "plt.scatter(y_test,predictions)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x24828e39940>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              "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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot((y_test-predictions),bins=50)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x24828e39ef0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.0641341418003\n",
            "MSE: 0.0120502895138\n",
            "RMSE: 0.109773810692\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.5.5",
      "nbconvert_exporter": "python",
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "pygments_lexer": "ipython3",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      }
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.14.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}